olsrr (version 0.1.0)

ols_step_forward: Stepwise Forward Regression

Description

Build regression model from a set of candidate predictor variables by entering predictors based on p values, in a stepwise manner until there is no variable left to enter any more.

Usage

ols_step_forward(model, ...)
# S3 method for ols_step_forward
plot(x, model = NA, ...)

Arguments

model

an object of class lm; the model should include all candidate predictor variables

...

other arguments

x

an object of class ols_step_forward

Value

ols_step_forward returns an object of class "ols_step_forward". An object of class "ols_step_forward" is a list containing the following components:

steps

f statistic

predictors

p value of score

rsquare

degrees of freedom

aic

fitted values of the regression model

sbc

name of explanatory variables of fitted regression model

sbic

response variable

adjr

predictors

rmse

predictors

mallows_cp

predictors

indvar

predictors

References

Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

Examples

# stepwise forward regression
model <- lm(y ~ ., data = surgical)
ols_step_forward(model)

# stepwise forward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_forward(model)
plot(k)